Multivariate Statistical Analysis in the Real and Complex Domains
Mathai, Arak M.
Multivariate Statistical Analysis in the Real and Complex Domains - Cham Springer Nature 2022 - 1 electronic resource (912 p.)
Open Access
This book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward. This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. It can also be used in advanced courses on multivariate analysis. Numerous exercises are included throughout.
Creative Commons
English
978-3-030-95864-0 9783030958640
10.1007/978-3-030-95864-0 doi
Applied mathematics
Probability & statistics
Statistical physics
classifications cluster complex domain factor analysis Gaussian distributions mathematical statistics matrix-variate multivariate statistical analysis profile analyses type-1 distributions type-2 distributions Wishart distribution
Multivariate Statistical Analysis in the Real and Complex Domains - Cham Springer Nature 2022 - 1 electronic resource (912 p.)
Open Access
This book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward. This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. It can also be used in advanced courses on multivariate analysis. Numerous exercises are included throughout.
Creative Commons
English
978-3-030-95864-0 9783030958640
10.1007/978-3-030-95864-0 doi
Applied mathematics
Probability & statistics
Statistical physics
classifications cluster complex domain factor analysis Gaussian distributions mathematical statistics matrix-variate multivariate statistical analysis profile analyses type-1 distributions type-2 distributions Wishart distribution